# Copyright (c) OpenMMLab. All rights reserved. import argparse import os import os.path as osp import mmcv import mmengine from mmocr.utils import crop_img, dump_ocr_data def collect_files(img_dir, gt_dir): """Collect all images and their corresponding groundtruth files. Args: img_dir (str): The image directory gt_dir (str): The groundtruth directory Returns: files (list): The list of tuples (img_file, groundtruth_file) """ assert isinstance(img_dir, str) assert img_dir assert isinstance(gt_dir, str) assert gt_dir ann_list, imgs_list = [], [] for img_file in os.listdir(img_dir): ann_file = 'gt_' + str(int(img_file[2:6])) + '.txt' ann_list.append(osp.join(gt_dir, ann_file)) imgs_list.append(osp.join(img_dir, img_file)) files = list(zip(imgs_list, ann_list)) assert len(files), f'No images found in {img_dir}' print(f'Loaded {len(files)} images from {img_dir}') return files def collect_annotations(files, nproc=1): """Collect the annotation information. Args: files (list): The list of tuples (image_file, groundtruth_file) nproc (int): The number of process to collect annotations Returns: images (list): The list of image information dicts """ assert isinstance(files, list) assert isinstance(nproc, int) if nproc > 1: images = mmengine.track_parallel_progress( load_img_info, files, nproc=nproc) else: images = mmengine.track_progress(load_img_info, files) return images def load_img_info(files): """Load the information of one image. Args: files (tuple): The tuple of (img_file, groundtruth_file) Returns: img_info (dict): The dict of the img and annotation information """ assert isinstance(files, tuple) img_file, gt_file = files assert int(osp.basename(gt_file)[3:-4]) == int( osp.basename(img_file)[2:-4]) # read imgs while ignoring orientations img = mmcv.imread(img_file, 'unchanged') img_info = dict( file_name=osp.join(osp.basename(img_file)), height=img.shape[0], width=img.shape[1], segm_file=osp.join(osp.basename(gt_file))) if osp.splitext(gt_file)[1] == '.txt': img_info = load_txt_info(gt_file, img_info) else: raise NotImplementedError return img_info def load_txt_info(gt_file, img_info): """Collect the annotation information. The annotation format is as the following: x1,y1,x2,y2,x3,y3,x4,y4,text 118,15,147,15,148,46,118,46,LƯỢNG 149,9,165,9,165,43,150,43,TỐT 167,9,180,9,179,43,167,42,ĐỂ 181,12,193,12,193,43,181,43,CÓ 195,13,215,14,215,46,196,46,VIỆC 217,13,237,14,239,47,217,46,LÀM, Args: gt_file (str): The path to ground-truth img_info (dict): The dict of the img and annotation information Returns: img_info (dict): The dict of the img and annotation information """ with open(gt_file, encoding='utf-8') as f: anno_info = [] for line in f: line = line.strip('\n') ann = line.split(',') bbox = ann[0:8] word = line[len(','.join(bbox)) + 1:] bbox = [int(coord) for coord in bbox] # Ignore hard samples if word == '###': continue assert len(bbox) == 8 anno = dict(bbox=bbox, word=word) anno_info.append(anno) img_info.update(anno_info=anno_info) return img_info def generate_ann(root_path, split, image_infos, preserve_vertical): """Generate cropped annotations and label txt file. Args: root_path (str): The root path of the dataset split (str): The split of dataset. Namely: training or test image_infos (list[dict]): A list of dicts of the img and annotation information preserve_vertical (bool): Whether to preserve vertical texts """ dst_image_root = osp.join(root_path, 'crops', split) ignore_image_root = osp.join(root_path, 'ignores', split) if split == 'training': dst_label_file = osp.join(root_path, 'train_label.json') elif split == 'test': dst_label_file = osp.join(root_path, 'test_label.json') elif split == 'unseen_test': dst_label_file = osp.join(root_path, 'unseen_test_label.json') os.makedirs(dst_image_root, exist_ok=True) img_info = [] for image_info in image_infos: index = 1 src_img_path = osp.join(root_path, 'imgs', split, image_info['file_name']) image = mmcv.imread(src_img_path) src_img_root = image_info['file_name'].split('.')[0] for anno in image_info['anno_info']: word = anno['word'] dst_img = crop_img(image, anno['bbox'], 0, 0) h, w, _ = dst_img.shape dst_img_name = f'{src_img_root}_{index}.png' index += 1 # Skip invalid annotations if min(dst_img.shape) == 0: continue # Skip vertical texts if not preserve_vertical and h / w > 2 and split == 'training': dst_img_path = osp.join(ignore_image_root, dst_img_name) mmcv.imwrite(dst_img, dst_img_path) continue dst_img_path = osp.join(dst_image_root, dst_img_name) mmcv.imwrite(dst_img, dst_img_path) img_info.append({ 'file_name': dst_img_name, 'anno_info': [{ 'text': word }] }) dump_ocr_data(img_info, dst_label_file, 'textrecog') def parse_args(): parser = argparse.ArgumentParser( description='Generate training and test set of VinText ') parser.add_argument('root_path', help='Root dir path of VinText') parser.add_argument( '--preserve-vertical', help='Preserve samples containing vertical texts', action='store_true') parser.add_argument( '--nproc', default=1, type=int, help='Number of processes') args = parser.parse_args() return args def main(): args = parse_args() root_path = args.root_path for split in ['training', 'test', 'unseen_test']: print(f'Processing {split} set...') with mmengine.Timer( print_tmpl='It takes {}s to convert VinText annotation'): files = collect_files( osp.join(root_path, 'imgs', split), osp.join(root_path, 'annotations')) image_infos = collect_annotations(files, nproc=args.nproc) generate_ann(root_path, split, image_infos, args.preserve_vertical) if __name__ == '__main__': main()